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Range-filtering approximate nearest neighbor search (RFANNS) has gained significant attention recently. Consider a setDof high-dimensional vectors, each associated with a numeric attribute value, e.g., price or timestamp. An RFANNS query consists of a query vectorqand a query range, reporting the approximate nearest neighbors ofqamong data vectors whose attributes fall in the query range. Existing work on RFANNS only considers a static setDof data vectors while in many real-world scenarios, vectors arrive in the system in an arbitrary order. This paper studies dynamic RFANNS where both data vectors and queries arrive in a mixed stream: a query is posed on all the data vectors that have already arrived in the system. Existing work on RFANNS is difficult to be extended to the streaming setting as they construct the index in the order of the attribute values while the vectors arrive in the system in an arbitrary order. The main challenge to the dynamic RFANNS lies in the difference between the two orders. A naive approach to RFANNS maintains multiple hierarchical navigable small-world (HNSW) graphs, one for each of theO(|D|²) possible query ranges - too expensive to construct and maintain. To design an index structure that can integrate new data vectors with a low index size increment for efficient and effective query processing, we propose a structure calleddynamic segment graph.It compresses the set of HNSW graphs of the naive approach, proven to be lossless under certain conditions, with only a linear to log |D| new edges in expectation when inserting a new vector. This dramatically reduces the index size while largely preserving the search performance. We further propose heuristics to significantly reduce the index cost of our dynamic segment graph in practice. Extensive experimental results show that our approach outperforms existing methods for static RFANNS and is scalable in handling dynamic RFANNS. 

Pentacene thin-films OFETs show increased conductance and mobility after exposure to maleic anhydride which shifts the mean energy in the grain boundaryviaan applied dipole. 

In contemporary database applications, the demand for memory resources is intensively high. To enhance adaptability to varying resource needs and improve cost efficiency, the integration of diverse storage technologies within heterogeneous memory architectures emerges as a promising solution. Despite the potential advantages, there exists a significant gap in research related to the security of data within these complex systems. This paper endeavors to fill this void by exploring the intricacies and challenges of ensuring data security in object-oriented heterogeneous memory systems. We introduce the concept of Unified Encrypted Memory (UEM) management, a novel approach that provides unified object references essential for data management platforms, while simultaneously concealing the complexities of physical scheduling from developers. At the heart of UEM lies the seamless and efficient integration of data encryption techniques, which are designed to ensure data integrity and guarantee the freshness of data upon access. Our research meticulously examines the security deficiencies present in existing heterogeneous memory system designs. By advancing centralized security enforcement strategies, we aim to achieve efficient object-centric data protection. Through extensive evaluations conducted across a variety of memory configurations and tasks, our findings highlight the effectiveness of UEM. The security features of UEM introduce low and acceptable overheads, and UEM outperforms conventional security measures in terms of speed and space efficiency. 

Effective vector representation models, e.g., word2vec and node2vec, embed real-world objects such as images and documents in high dimensional vector space. In the meanwhile, the objects are often associated with attributes such as timestamps and prices. Many scenarios need to jointly query the vector representations of the objects together with their attributes. These queries can be formalized as range-filtering approximate nearest neighbor search (ANNS) queries. Specifically, given a collection of data vectors, each associated with an attribute value whose domain has a total order. The range-filtering ANNS consists of a query range and a query vector. It finds the approximate nearest neighbors of the query vector among all the data vectors whose attribute values fall in the query range. Existing approaches suffer from a rapidly degrading query performance when the query range width shifts. The query performance can be optimized by a solution that builds an ANNS index for every possible query range; however, the index time and index size become prohibitive -- the number of query ranges is quadratic to the number n of data vectors. To overcome these challenges, for the query range contains all attribute values smaller than a user-provided threshold, we design a structure called the segment graph whose index time and size are the same as a single ANNS index, yet can losslessly compress the n ANNS indexes, reducing the indexing cost by a factor of Ω(n). To handle general range queries, we propose a 2D segment graph with average-case index size O(n log n) to compress n segment graphs, breaking the quadratic barrier. Extensive experiments conducted on real-world datasets show that our proposed structures outperformed existing methods significantly; our index also exhibits superior scalability. 

Abstract The available spatial data are rapidly growing and also diversifying. One may obtain in large quantities information such as annotated point/place of interest (POIs), check-in comments on those POIs, geo-tagged microblog comments, and demarked regions of interest (ROI). All sources interplay with each other, and together build a more complete picture of the spatial and social dynamics at play in a region. However, building a single fused representation of these data entries has been mainly rudimentary, such as allowing spatial joins. In this paper, we extend the concept of semantic embedding for POIs (points of interests) and devise the first semantic embedding of ROIs, and in particular ones that captures both its spatial and its semantic components. To accomplish this, we develop a multipart network model capturing the relationships between the diverse components, and through random-walk-based approaches, use this to embed the ROIs. We demonstrate the effectiveness of this embedding at simultaneously capturing both the spatial and semantic relationships between ROIs through extensive experiments. Applications like popularity region prediction demonstrate the benefit of using ROI embedding as features in comparison with baselines. 

Increasingly, individuals and companies adopt a cloud service provider as a primary data and IT infrastructure platform. The remote access of the data inevitably brings the issue of trust. Data encryption is necessary to keep sensitive information secure and private on the cloud. Yet adversaries can still learn valuable information regarding encrypted data by observing data access patterns. To solve such problem, Oblivious RAMs (ORAMs) are proposed to completely hide access patterns. However, most ORAM constructions are expensive and not suitable to deploy in a database for supporting query processing over large data. Furthermore, an ORAM processes queries synchronously, hence, does not provide high throughput for concurrent query processing. In this work, we design a practical oblivious query processing framework to enable efficient query processing over a cloud database. In particular, we focus on processing multiple range and kNN queries asynchronously and concurrently with high throughput. The key idea is to integrate indices into ORAM which leverages a suite of optimization techniques (e.g., oblivious batch processing and caching). The effectiveness and efficiency of our oblivious query processing framework is demonstrated through extensive evaluations over large datasets. Our construction shows an order of magnitude speedup in comparison with other baselines. 

Nowadays an emerging class of applications are based oncollaboration over a shared database among different entities. However, the existing solutions on shared database may require trust on others, have high hardware demand that is unaffordable for individual users, or have relatively low performance. In other words, there is a trilemma among security, compatibility and efficiency. In this paper, we present FalconDB, which enables different parties with limited hardware resources to efficiently and securely collaborate on a database. FalconDB adopts database servers with verification interfaces accessible to clients and stores the digests for query/update authentications on a blockchain. Using blockchain as a consensus platform and a distributed ledger, FalconDB is able to work without any trust on each other. Meanwhile, FalconDB requires only minimal storage cost on each client, and provides anywhere-available, real-time and concurrent access to the database. As a result, FalconDB over-comes the disadvantages of previous solutions, and enables individual users to participate in the collaboration with high efficiency, low storage cost and blockchain-level security guarantees. 

Morphology plays a critical role in determining the properties of solid-state molecular materials, yet fluctuates wildly as these materials undergo reaction. A prototypical system, a vapor–solid Diels–Alder reaction of tetracene and pentacene thin-films, is used to observe the evolution of morphology features as the reaction transitions from surface to bulk. The initial stages of reaction display little topographical change as measured by atomic force microscopy (AFM) and scanning electron microscopy (SEM), and substrates are coated with a uniform layer of product 1–2 molecules thick, as determined by energy-dispersive X-ray (EDX) spectroscopy. The highly textured surfaces of late stage reactions are a result of aggregated products, as identified via EDX spectroscopy and polarization modulation infrared reflection absorption spectroscopy (PM-IRRAS); areas of the surface in between product aggregates resemble the initial stages. The mechanism by which products aggregate into surface asperities requires the assistance of a facilitating media – in this case condensed vapor; simple thermally assisted surface diffusion was unable to generate these morphology changes. The combined data indicate that reactions of molecular solids, could be confined to the surface in the absence of condensate of the vapor phase reactant.